Evaluating OMA System Identification Techniques for Drive-by Health Monitoring on Short Span Bridges

Locke, William R.; Redmond, Laura; Schmid, Matthias

doi:10.1061/(asce)be.1943-5592.0001923

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…In the reference [13], the authors successfully identified the bridge's fundamental frequency using a mass-spring model, and later the idea was verified by Lin and Yang in a field test [15] using a tractor-trailer system on Da-Wu-Lun Bridge in Taiwan. Thereafter, identifying the bridge's natural frequencies from the vehicle's accelerations was also investigated using numerical simulations [16][17][18][19], laboratory experiments [20][21][22], and field tests [23][24][25][26][27]. Apart from the bridge's frequencies, it was found that modal shapes [28][29][30][31][32][33] and damping ratios [34][35][36][37][38] could also be extracted from the passing vehicle's vibrations.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Frequency-Domain Dimension Reduction for A2M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles

Lan

Lin

2023

Materials

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Recent decades have witnessed a rise in interest in bridge health monitoring utilizing the vibrations of passing vehicles. However, existing studies commonly rely on constant speeds or tuning vehicular parameters, making their methods challenging to be used in practical engineering applications. Additionally, recent studies on the data-driven approach usually need labeled data for damage scenarios. Still, getting these labels in engineering is difficult or even impractical because the bridge is typically in a healthy state. This paper proposes a novel, damaged-label-free, machine-learning-based, indirect bridge-health monitoring method named the assumption accuracy method (A2M). Initially, the raw frequency responses of the vehicle are employed to train a classifier, and K-folder cross-validation accuracy scores are then used to calculate a threshold to specify the bridge’s health state. Compared to merely focusing on low-band frequency responses (0–50 Hz), utilizing full-band vehicle responses can significantly improve the accuracy, meaning that the bridge’s dynamic information exists in the higher frequency ranges and can contribute to detecting bridge damage. However, raw frequency responses are generally in a high-dimensional space, and the number of features is much greater than that of samples. To represent the frequency responses via latent representations in a low-dimension space, appropriate dimension-reduction techniques are therefore, needed. It was found that principal component analysis (PCA) and Mel-frequency cepstral coefficients (MFCCs) are suitable for the aforementioned issue, and MFCCs are more damage-sensitive. When the bridge is in a healthy condition, the accuracy values obtained using MFCCs are primarily dispersed around 0.5, but following the occurrence of damage, they increased significantly to 0.89–1.0 in this study.

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Section: Introductionmentioning

confidence: 99%

Investigation of Frequency-Domain Dimension Reduction for A2M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles

Lan

Lin

2023

Materials

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“…Stochastic subspace identifcation (SSI), one of the powerful frequency identifcation tools for frequency identifcation, was frst adopted [18]. Other tools such as peak picking (PP) and frequency domain decomposition (FDD) were also proved efective for identifying the frequencies of the vehicle and bridge [19]. Notwithstanding, it was always difcult to identify the bridge's frequencies using one vehicle passage in the frequency domain due to the infuence of vehicle frequencies and road roughness.…”

Section: Introductionmentioning

confidence: 99%

Bridge Frequency Scanning Using the Contact-Point Response of an Instrumented 3D Vehicle: Theory and Numerical Simulation

Lin

Zhang

2023

Structural Control and Health Monitoring

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Scanning the bridge’s frequencies from the passing vehicle’s vibration data has been frequently investigated recently. However, in previous studies, vehicles were typically simplified to quarter- or half-car models, and apparent disparity could be observed between the models and real vehicles. To make the vehicle model more practical, in this study, a 3D vehicle model is built to extract the bridge’s frequencies from vehicle vibrations. For the first time, equations for calculating the contact-point (CP) response of the 3D vehicle model are derived with tire damping. Furthermore, residual CP responses between front and rear wheels are utilized to eliminate the inverse effects of road roughness, making the bridge frequencies outstanding in the frequency domain. The robustness of the proposed method is tested under different influence factors, and two possible measurement errors are as follows: the sensor position and axle distance when applying the proposed method in engineering. Results show that the proposed method performs stably under the influence of different road roughness classes and tire damping. Bridge frequencies can be identified when the vehicle is travelling at a highway speed (108 km/h in this study). Environmental noises can submerge the bridge’s high-order frequencies but have little influence on the low-frequency range. High bridge damping will restrain the transmission of bridge vibration to the vehicle, making high-order bridge frequencies less visible. In addition, the errors introduced by a vehicle body sensor position can be eliminated when calculating the CP responses for tires, thus will not influence bridge frequency identification. To avoid possible errors induced by manual measurement of the axle distance, a novel cross-correlation function-based method is employed, which is verified effective and practical for calculating residual CP responses.

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“…[1][2][3][4] It can be seen that Bridges are very important in the social operation system. [5][6][7] At the same time, the safety and durability level of the bridge itself is also directly related to the operation and development of the society. [8][9][10] For example, the Eigongyan cross-river rail bridge in Chongqing was interrupted by a broken cable.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of social economy and the people's need for a better life, more and more Bridges have been built to avoid traffic congestion and improve the people's travel efficiency 1–4 . It can be seen that Bridges are very important in the social operation system 5–7 . At the same time, the safety and durability level of the bridge itself is also directly related to the operation and development of the society 8–10 .…”

Section: Introductionmentioning

confidence: 99%

Research on mapping relationship between environmental load and vibration response of bridge structure based on structural health monitoring data

Qiao,

Ding,

Zhou

et al. 2023

Engineering Reports

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With the increasing lifespan of bridges, various forms of deterioration will inevitably occur. The bridge health monitoring system enables direct monitoring of the loads (such as wind, temperature, and vehicles) experienced by the bridge during operation, as well as the corresponding vibration response of the bridge structure (including main beam deformation, tower top displacement, cable acceleration, etc.). These data provide valuable insights into the safety and durability of the bridge during its operational lifetime. However, the challenge lies in extracting meaningful information from the vast amount of collected data. This article focuses on the Fuyu Bridge as a research case and analyzes the variations in environmental factors, such as temperature, humidity, wind load, and structural vibration responses (main beam and cable), obtained from the Fuyu Bridge's structural health monitoring system. It also conducts a structural safety assessment during the operational period of the bridge. Furthermore, the research explores the relationship between environmental factors, load effects, and structural response to establish a foundation for future structural safety assessments of the Fuyu Bridge during its operational period. For instance, changes in environmental factors or load effects can be used to predict structural responses. Additionally, this study provides access to the structural health monitoring data of the Fuyu Bridge, facilitating data‐driven research for other scholars in the field.

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Evaluating OMA System Identification Techniques for Drive-by Health Monitoring on Short Span Bridges

Cited by 11 publications

References 34 publications

Investigation of Frequency-Domain Dimension Reduction for A2M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles

Investigation of Frequency-Domain Dimension Reduction for A2M-Based Bridge Damage Detection Using Accelerations of Moving Vehicles

Bridge Frequency Scanning Using the Contact-Point Response of an Instrumented 3D Vehicle: Theory and Numerical Simulation

Research on mapping relationship between environmental load and vibration response of bridge structure based on structural health monitoring data

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